Face Sketch Colorization via Supervised GANs

Abstract

Face sketch recognition is one of the most challenging heterogeneous face recognition problems. The large domain difference of hand-drawn sketches and color photos along with the subjectivity/variations due to eye-witness descriptions and skill of sketch artists makes the problem demanding. Therefore, despite several research attempts, sketch to photo matching is still considered an arduous problem. In this research, we propose to transform a hand-drawn sketch to a color photo using an end to end two-stage generative adversarial model followed by learning a discriminative classifier for matching the transformed images with color photos. The proposed image to image transformation model reduces the modality gap of the sketch images and color photos resulting in higher identification accuracies and images with better visual quality than the ground truth sketch images.